Google's chief executive, Sundar Pichai, testified before the House judiciary committee on Tuesday morning, three months after his company thumbed its nose at Congress by failing to appear alongside Facebook and Twitter at a Senate hearing on election interference. In a hearing heavy on partisan theatrics, Pichai notably refused to rule out launching a censored search engine in China, a controversial plan that has garnered significant criticism from human rights organizations as well as rank-and-file Google employees. "Right now there are no plans to launch search in China," Pichai said numerous times, repeating a talking point that the company has relied on since news of the project leaked in August. Pichai characterized the Chinese search product as an "internal effort" and said the company would be "transparent" and consult with policy makers before launching in China. Pressed to rule out launching a tool that would enable censorship and surveillance in China, however, Pichai appeared to offer the company's probable justification for reentering a market that it left in 2010: "We think it's in our duty to explore possibilities to give users access to information."

Google has'no plans' to relaunch a search engine in China though it is continuing to study the idea, Chief Executive Sundar Pichai told a U.S. congressional panel on Tuesday amid increased scrutiny of big tech firms. Lawmakers and Google employees have raised concerns the company would comply with China's internet censorship and surveillance policies if it re-enters the Asian nation's search engine market. Google's main search platform has been blocked in China since 2010, but the Alphabet Inc unit has been attempting to make new inroads into the country, which has the world's largest number of smartphone users. Chief Executive Sundar Pichai told a U.S. congressional panel Google had over 100 people working on the project at one point. 'Right now, there are no plans to launch search in China,' Pichai told the U.S. House of Representatives Judiciary Committee.

Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms. Accordingly, there is a pressing need for robust tools to detect and classify Islamophobic hate speech at scale. Previous research has largely approached the detection of Islamophobic hate speech on social media as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically-informed social science and effectively monitoring social media. Drawing on in-depth conceptual work we build a multi-class classifier which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. We apply the classifier to a dataset of 109,488 tweets produced by far right Twitter accounts during 2017. Whilst most tweets are not Islamophobic, weak Islamophobia is considerably more prevalent (36,963 tweets) than strong (14,895 tweets). Our main input feature is a gloVe word embeddings model trained on a newly collected corpus of 140 million tweets. It outperforms a generic word embeddings model by 5.9 percentage points, demonstrating the importan4ce of context. Unexpectedly, we also find that a one-against-one multi class SVM outperforms a deep learning algorithm.

If you were hoping that Google chief Sundar Pichai would shed more light on his company's potential censored search engine for China... well, you'll mostly be disappointed. Rhode Island Representative David Cicilline grilled Pichai on the recently acknowledged Dragonfly project and mostly encountered attempts to downplay the significance of the engine. The Google exec stressed there were "no plans" to launch a search engine for China, and that Dragonfly was an "internal effort" and "limited" in scope. Pichai added that Google was "currently not in discussions" with Chinese officials. He also provided a non-committal answer when asked if Google would promise not to create a tool enabling Chinese surveillance.

Abstract-- In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world. I. INTRODUCTION He number of daily-generated images by websites and personal archives are constantly growing. Indeed, the effective management of the rapid expansion of visual information has become a major problem and a necessity for strengthening visual search technique based on visual content [3]. This necessity is behind the emergence of new visual search techniques based on visual content. It has been widely identified that the most efficient and intuitive way to research visual information is based on the properties that are extracted from the images themselves. Researchers from different communities ("Computer Vision" [4], "Database Management", "Man-machine Interface", "Information Retrieval") were attracted by this field. Since then, the search for images by content has developed quite rapidly. The intuitive idea of "any system that analyzes or automatically organizes a set of data or knowledge must use, in one form or another, a similarity operator whose purpose is to establish similarities or the relationships that exist between the manipulated information".

Machine playtesting tools and game moment search engines require exposure to the diversity of a game's state space if they are to report on or index the most interesting moments of possible play. Meanwhile, mobile app distribution services would like to quickly determine if a freshly-uploaded game is fit to be published. Having access to a semantic map of reachable states in the game would enable efficient inference in these applications. However, human gameplay data is expensive to acquire relative to the coverage of a game that it provides. We show that off-the-shelf automatic exploration strategies can explore with an effectiveness comparable to human gameplay on the same timescale. We contribute generic methods for quantifying exploration quality as a function of time and demonstrate our metric on several elementary techniques and human players on a collection of commercial games sampled from multiple game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of states reached and the semantic map extracted, this work makes productive contrast with the focus on finding a behavior policy or optimizing game score used in most automatic game playing research.

We propose a new class of data-independent locality-sensitive hashing (LSH) algorithms based on the fruit fly olfactory circuit. The fundamental difference of this approach is that, instead of assigning hashes as dense points in a low dimensional space, hashes are assigned in a high dimensional space, which enhances their separability. We show theoretically and empirically that this new family of hash functions is locality-sensitive and preserves rank similarity for inputs in any `p space. We then analyze different variations on this strategy and show empirically that they outperform existing LSH methods for nearest-neighbors search on six benchmark datasets. Finally, we propose a multi-probe version of our algorithm that achieves higher performance for the same query time, or conversely, that maintains performance of prior approaches while taking significantly less indexing time and memory. Overall, our approach leverages the advantages of separability provided by high-dimensional spaces, while still remaining computationally efficient

Semantic Web Rule Language (SWRL) combines OWL (Web Ontology Language) ontologies with Horn Logic rules of the Rule Markup Language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not go-ing to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPIN (SPARQL Inferencing Notation) has become a de-facto industry standard to rep-resent SPARQL rules and constraints on Semantic Web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this paper, we ar-gue that the life of existing SWRL rule-based ontology applications can be prolonged by con-verting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.

Businesses will be ready for Web 3.0 and web 4.0 evolution which is connecting all devices in the real and virtual world in real-time.This will be more prominent in 2019. By 2020 we will be witnessed to the web 4.0 aka smart web when SEO will be less time consuming and easy. Defamation will be more ineffective and targeted bullying against brands, politicians, influencers and individuals will lose its power on social network. Since some advertising tools is equipped with Blockchain technology, there will be some improvements in digital marketing processes. Here are some insights on how artificial intelligence and Blockchain will shape up SEO and digital marketing in 2019.

The first time many of us heard about China's use of facial recognition on jaywalkers was just this week when a prominent Chinese businesswoman was publicly "named and shamed" for improper street crossing. Turns out, she wasn't even there: China's terrifyingly over-the-top use of tech for citizen surveillance made a mistake. The AI system identified Dong Mingzhu's face from a bus advertisement for her company's products. "[The] president of China's biggest air conditioning maker," wrote The Telegraph, "had her image flashed up on a public display screen in the city of Ningbo, near Shanghai, with a caption saying she had illegally crossed the street on a red light." Shortly after, Ningbo traffic police admitted the mistake and claimed to have "completely upgraded the system to reduce the false recognition rate."